Github Vllm Project Vllm Omni A Framework For Efficient Model

| Documentation | User Forum | Developer Slack | vLLM was originally designed to support large language models for text-based autoregressive generation tasks. vLLM-Omni is a framework that extends its support for omni-modality model inference and serving: vLLM-Omni is flexible and easy to use with: vLLM-Omni seamlessly supports most popular open-source models on HuggingFace, including: We welcome and value any contributions and collaborations.

Please check out Contributing to vLLM-Omni for how to get involved. vLLM was originally designed to support large language models for text-based autoregressive generation tasks. vLLM-Omni is a framework that extends its support for omni-modality model inference and serving: vLLM-Omni is flexible and easy to use with: vLLM-Omni seamlessly supports most popular open-source models on HuggingFace, including: For more information, checkout the following: - vllm-omni announcement blogpost

A framework for high-performance, cost-efficient inference and serving of omni-modality models across text, image, video, and audio. vLLM-Omni is a framework designed for inference and serving of omni-modality models, supporting text, image, video, and audio inputs as well as heterogeneous outputs. Built on vLLM’s efficient inference foundations, vLLM-Omni extends support to non-autoregressive architectures (e.g., Diffusion Transformers) and parallel generation models, enabling production-grade deployment with improved throughput and cost efficiency. High-throughput, memory-efficient inference and serving engine for large … A Next.js web application that integrates AI into draw.io to support … An open-source streaming dialogue text-to-speech (TTS) model and inference …

The vLLM team has released the first "omnimodal" inference framework, vLLM-Omni, turning the unified generation of text, images, audio, and video from a concept prototype into practical code. The new framework is now available on GitHub and ReadTheDocs, allowing developers to immediately install and call it via pip. - Modal Encoder: ViT, Whisper, etc., are responsible for converting vision and speech into intermediate features - LLM Core: continues to use the vLLM autoregressive engine, handling thinking, planning, and dialogue - Modal Generator: diffusion models such as DiT and Stable Diffusion decode outputs, supporting synchronized generation of images, audio, and video The framework treats the three components as independent microservices, which can be scheduled across different GPUs or nodes, enabling elastic scaling according to demand—expanding DiT during image generation peaks and shrinking LLM during text...

Have you ever wondered how AI-powered applications like chatbots, code assistants and more respond so quickly? Or perhaps you’ve experienced the frustration of waiting for a large language model (LLM) to generate a response, wondering what’s taking so long. Well, behind the scenes, there’s an open source project aimed at making inference, or responses from models, more efficient. vLLM, originally developed at UC Berkeley, is specifically designed to address the speed and memory challenges that come with running large AI models. It supports quantization, tool calling and a smorgasbord of popular LLM architectures (Llama, Mistral, Granite, DeepSeek—you name it). Let’s learn the innovations behind the project, why over 40k developers have starred the project on GitHub and how to get started with vLLM today!

As detailed in our vLLM introductory article, serving an LLM requires an incredible amount of calculations to be performed to generate each word of their response. This is unlike other traditional workloads, and can be often expensive, slow and memory intensive. For those wanting to run LLMs in production, this includes challenges such as: With the need for LLM serving to be affordable and efficient, vLLM arose from a research paper called, “Efficient Memory Management for Large Language Model Serving with Paged Attention," from September of 2023, which... The results? Up to 24x throughput improvements compared to similar systems such as HuggingFace Transformers and Text Generation Inference (TGI), with much less KV cache waste.

Let’s briefly touch on the techniques used by vLLM in order to improve performance and efficiently utilize GPU resources: There was an error while loading. Please reload this page. Core Question Addressed: How can we efficiently serve the next generation of AI models that process and generate text, images, audio, and video, overcoming the limitations of serving engines designed only for text-based Autoregressive... The landscape of generative AI is undergoing a profound transformation. Models are rapidly evolving from specialized Large Language Models (LLMs) to powerful “omni-agents” capable of seamlessly reasoning across and generating content in text, images, audio, and video modalities.

This shift—from “text-in, text-out” to complex, heterogeneous input and output—demands an equally revolutionary shift in the underlying infrastructure. Serving these cutting-edge omni-modality models presents unique challenges that traditional serving engines, highly optimized for text-based Autoregressive (AR) tasks, cannot adequately handle. We are excited to introduce vLLM-Omni, a major extension of the renowned vLLM ecosystem. It stands as one of the first open-source frameworks specifically engineered to extend vLLM’s exceptional performance—namely, its high-throughput and memory-efficient serving—to the entire world of multi-modal and non-autoregressive inference. vLLM-Omni is designed to make omni-modality model serving accessible, efficient, and cost-effective for everyone. We are excited to announce the official release of vLLM-Omni, a major extension of the vLLM ecosystem designed to support the next generation of AI: omni-modality models.

Since its inception, vLLM has focused on high-throughput, memory-efficient serving for Large Language Models (LLMs). However, the landscape of generative AI is shifting rapidly. Models are no longer just about text-in, text-out. Today’s state-of-the-art models reason across text, images, audio, and video, and they generate heterogeneous outputs using diverse architectures. vLLM-Omni is one of the first open source frameworks to support omni-modality model serving that extends vLLM’s exceptional performance to the world of multi-modal and non-autoregressive inference. Traditional serving engines were optimized for text-based Autoregressive (AR) tasks.

As models evolve into “omni” agents—capable of seeing, hearing, and speaking—the serving infrastructure must evolve with them. vLLM-Omni addresses three critical shifts in model architecture:

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